Biometric identification is a method for identifying individuals using measurable biometric identifiers (or ‘traits’), such as their face, DNA, or fingerprint. The method is often used to identify or verify the identity of an individual. Many biometric identifiers, such as DNA, have a high level of uniqueness and reliability when identifying or verifying the identity of individuals. However, face recognition technology suffers from a relatively low reliability, particularly due to the likelihood of a face or its expression changing over time (i.e. a lack of ‘permanence’ in the terminology of the art).
Although relatively unreliable, face recognition is still a popular biometric as it may be implemented using basic technology (e.g. an imaging sensor coupled to a computing device), may be carried out quickly, and may be used without the target's knowledge. Accordingly, many solutions have been proposed to improve the performance of face recognition algorithms. These solutions can generally be grouped into one of two categories—control, in which the image capture environment is optimized, or mitigation, in which the adverse effects of any factors present in the image are minimized.
Control based solutions are particularly applicable in cooperative user applications (such as access control, token-based identification verification and computer logon), in which a user cooperates with the image capture process. In such situations, the image capture environment may be tightly controlled, such that lighting, the user's pose, and the user's expression may be directed or influenced to improve the reliability of the face recognition algorithm.
US Patent Application Publication no. 2013/0215275 A1 discloses an enrolment kiosk implementing several control based solutions to improve the reliability of a face recognition algorithm. The enrolment kiosk includes a screen and a camera, and a pair of LED light assemblies for illuminating the user during the face recognition process. The kiosk encourages the user to look at the camera, takes an initial image of the user's face, and analyses this initial image to determine certain qualities (for example, a comparison of the lighting level on one side of the face to the other side). Once analysed, the kiosk then controls the lighting assemblies such that the face is evenly illuminated, and takes a subsequent image to be used in the comparison with a training set of images during the face recognition process. The subsequent image, having a more even distribution of light, will reportedly improve the reliability of the face recognition algorithm.
Mitigation based solutions are particularly applicable to non-cooperative user applications (in which the user is not aware of the face recognition process, such as in surveillance), but are also used in combination with control based solutions in cooperative user applications. Mitigation techniques include pre-processing normalization of a training image set, the use of illumination-invariant face descriptors, the controlled inclusion of multiple poses within the training image set, and the use of 3D modelling and rotation of the training image set.
Articles “Image-Quality-Based Adaptive Face Recognition”, Sellahewa and Jassim, IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 4, April 2010, and “Face Recognition: The Problem of Compensating for Changes in Illumination Direction”, Adini, Moses and Ullman, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997, both discuss mitigation techniques for dealing with imperfect illumination of the user's face. In particular, the first article discloses adapting illumination normalization (a form of pre-processing of an input image) based on an estimate of the lighting conditions for the input image. In effect, it pre-processes the input image to better match the illumination of the training image. The second article gives a general discussion on various mitigation techniques for varying illumination and facial expressions.
The present inventors have recognized that the current techniques for increasing the reliability of face recognition algorithms may be improved, particularly when applied to interactive terminals (such as public-space touch-interactive information terminals). These terminals may offer automatic face recognition to identify a user and, once identified, personalize their displays to increase engagement with the user. However, there are several factors which make this difficult to implement in a consistently reliable manner. Firstly, the face recognition process is not a primary purpose for the terminal such that it is not appropriate to enforce the control based solutions (e.g. lighting or pose/expression control) on the user. For example, increasing the level of lighting can create an unfavourable user environment, either dazzling the user or creating harsh shadows. Secondly, interactive terminals are often placed indoors such that their exact location is usually based on factors other than optimal lighting for the face recognition process. Accordingly, the face recognition algorithm for interactive terminals suffers a poor level of recognition reliability.
It is therefore desirable to alleviate some or all of the above problems.